Significance Tests Research Articles

Evaluation of the accuracy (trueness, precision) and processing time of different 3-dimensional CAD software programs and algorithms for virtual cast alignment.

This in vitro study aimed to evaluate the impact of different alignment algorithms and CAD software programs on alignment accuracy (trueness and precision) and processing time. A mandibular typodont was digitized using a laboratory scanner (L2i) to obtain a reference STL (STLr) file. It was then scanned with an intraoral scanner (Primescan) and digitally duplicated ten times (n=10). Each scan was aligned with the STLr using 42 combinations of 3D CAD software and alignment algorithms. The tested software programs included Blender for Dental, BlueSkyPlan, Dental CAD App (Exocad), Medit Design, NemoSmile, and Meshmixer. Alignment accuracy (trueness and precision) and processing time were recorded using Python software (v3.8). Statistical analysis was performed with a two-way ANOVA test (α = .01) to identify overall differences, followed by a post hoc Tukey Honestly Significant Difference test (α = .05) to establish rankings. Significant differences in alignment accuracy were observed based on the software and algorithm used, affecting both trueness (p<.01) and precision (p<.01). Processing time also varied significantly (p<.01). Post hoc analysis identified the optimal algorithm for each software, revealing variations in trueness, precision, and processing time among the optimal versions. Medit Design achieved the best overall performance by combining high accuracy with the fastest processing time, while Meshmixer exhibited the lowest accuracy due to its lack of advanced algorithms. The choice of CAD software and alignment algorithm significantly influences alignment accuracy and efficiency. Best-fit and section-based provided the best results, offering valuable insights into the optimization of digital workflows in prosthodontics. Alignment protocols must be tailored to the specific CAD software program used, as no universal protocol was effective across all tested software. Optimizing alignment protocols reduces errors, enhances prosthodontic outcomes, and improves the reliability and efficiency of clinical and laboratory workflows, ultimately ensuring better patient care and treatment success.

CART-ANOVA-Based Transfer Learning Approach for Seven Distinct Tumor Classification Schemes with Generalization Capability

Background/Objectives: Deep transfer learning, leveraging convolutional neural networks (CNNs), has become a pivotal tool for brain tumor detection. However, key challenges include optimizing hyperparameter selection and enhancing the generalization capabilities of models. This study introduces a novel CART-ANOVA (Cartesian-ANOVA) hyperparameter tuning framework, which differs from traditional optimization methods by systematically integrating statistical significance testing (ANOVA) with the Cartesian product of hyperparameter values. This approach ensures robust and precise parameter tuning by evaluating the interaction effects between hyperparameters, such as batch size and learning rate, rather than relying solely on grid or random search. Additionally, it implements seven distinct classification schemes for brain tumors, aimed at improving diagnostic accuracy and robustness. Methods: The proposed framework employs a ResNet18-based knowledge transfer learning (KTL) model trained on a primary dataset, with 20% allocated for testing. Hyperparameters were optimized using CART-ANOVA analysis, and statistical validation ensured robust parameter selection. The model’s generalization and robustness were evaluated on an independent second dataset. Performance metrics, including precision, accuracy, sensitivity, and F1 score, were compared against other pre-trained CNN models. Results: The framework achieved exceptional testing accuracy of 99.65% for four-class classification and 98.05% for seven-class classification on the source 1 dataset. It also maintained high generalization capabilities, achieving accuracies of 98.77% and 96.77% on the source 2 datasets for the same tasks. The incorporation of seven distinct classification schemes further enhanced variability and diagnostic capability, surpassing the performance of other pre-trained models. Conclusions: The CART-ANOVA hyperparameter tuning framework, combined with a ResNet18-based KTL approach, significantly improves brain tumor classification accuracy, robustness, and generalization. These advancements demonstrate strong potential for enhancing diagnostic precision and informing effective treatment strategies, contributing to advancements in medical imaging and AI-driven healthcare solutions.

The Impact of Synectics Learning Model Implementation with Mind Mapping Assignments on Reducing Misconceptions and Enhancing Students' Cognitive Learning Outcomes

Innovative learning models are essential to enhance students' cognitive abilities and address misconceptions in physics learning. This study aims to provide an overview of the influence of implementing the Synectics learning model with mind mapping assignments on the quantity of misconceptions and the cognitive learning outcomes of senior high school students. The research employed a quasi-experimental method using a randomized control group pretest-posttest design. The study was conducted on 11th-grade students at a senior high school in Palu, Central Sulawesi, during the 2023/2024 academic year. A cluster random sampling technique was used to select the sample. Data collection was carried out using pretests and posttests to measure the improvement in cognitive learning outcomes, while posttests were used to assess misconceptions experienced by students during learning with the Synectics model with and without mind mapping assignments. Observation sheets were also used to monitor the implementation of the learning process. The results showed an improvement in cognitive abilities for the experimental class, with an average normalized gain (N-Gain) of 0.63 (high), compared to 0.50 (moderate) for the control class. Based on a significance test at the 0.05 level, the findings revealed that the Synectics learning model with mind mapping assignments significantly outperformed the Synectics model without such assignments. Additionally, descriptive analysis of the misconceptions indicated that the Synectics model with mind mapping assignments resulted in fewer misconceptions compared to the model without mind mapping assignments.

A high-resolution bovine mitochondrial co-expression network.

The mitochondrion is a sophisticated, versatile, and dynamic organelle whose function is incompletely understood. Intending to provide a framework for mitochondrial visualisation and interpretation of genome-wide molecular data, we reverse-engineered a co-expression network whose final structure represented mRNA encoding more than half of the entire mitochondrial proteome. We drew upon 723 RNA-seq data sets representing 91 tissues and cell types from 441 individual cattle. A mitochondrial landscape was formed comprising a main network and many smaller sub-networks. One of the discrete sub-networks contains all 13 mRNA (e.g. MT-ND1, MT -CYTB, MT -COX2, MT -ATP8) plus 15/22 tRNA (e.g. MT-TT) encoded by the mt-genome itself, indicating some independent regulation from the nuclear genome with whom it must cooperate. Intriguingly, this mtDNA sub-network also contains a single nuclear-encoded gene, that of PDHA1. PDHA1 encodes a subunit of the pyruvate dehydrogenase complex that governs the conversion of pyruvate to Acetyl CoA. This enzyme is extremely influential, representing the fundamental cellular connection between the ancient, conserved pathway of glycolysis that occurs exclusively in the cytoplasm, and the TCA cycle that occurs within the mitochondrial matrix. To demonstrate the downstream utility of our approach, we overlaid Longissimus dorsi muscle transcriptome data from differentially feed efficient Charolais and Holstein Friesian cattle. This approach highlighted expression patterns sensitive to both breed and diet in a complex manner. An analytic advantage of this approach is that relatively subtle (<2-fold) but coordinated changes that may be overlooked by conventional gene-by-gene significance testing become readily apparent. Finally, intending to understand the transcriptional regulation of mitochondrial function more thoroughly, we engineered a network built with transcription factors in addition to those mRNA encoding mitochondrial proteins. Here, a set of influential nuclear hormone receptors (e.g. PPARA) are enriched among the most highly and/or well-connected TF.

Long-Term Survival in Children Following Heart Transplantation.

Short-term outcomes following heart transplantation in children have improved, but comparable improvements in long-term survival continues to have barriers. We sought to investigate long-term outcomes following heart transplantation and to identify protective and risk factors associated with long-term survival in children. The Pediatric Heart Transplant Society (PHTS) database was queried for heart transplant recipients from 1993 to 2010 who were ≤ 10 years of age at time of transplant. Patients with conditional graft survival > 3 years and at ≥ 10 years were analyzed. Survival and time-to-event were compared using the Kaplan-Meier method with a log-rank test for significance. Factors associated with graft loss were identified using Cox proportional hazard modeling. There were 1610 patients ≤ 10 year of age who were transplanted between 1993 and 2010 with conditional survival to 3 years post-transplant. Of those patients, there were 1170 with conditional survival to 10 years post-transplant. Patients < 1 year at transplant had improved survival compared to other age groups. Risk factors for graft loss after 3 years post-transplant were malignancy, rejection, cardiac allograft vasculopathy (CAV), age, congenital heart disease, female sex, and Black race (p value for all < 0.05). Heart transplantation remains an effective therapy in children with a growing number of long-term survivors. Risk factors for mortality in patients ≤ 10 years of age at transplant with conditional survival to 3 years post-transplant include CAV, rejection, malignancy, female sex, and Black race. Further studies are needed to understand the social and biologic causes of racial and sex disparities in pediatric transplant patients.

The Influence of Individual Characteristics and Non-Physical Work Environment on Employee Performance

This study aims to determine the effect of individual characteristics and non-physical work environment on the performance of employees at the Praya Tengah Sub-District Office. The population in this study consists of all employees at the Praya Tengah Sub-District Office, with the sampling technique using purposive sampling. This type of research is associative quantitative research. Data processing in this study used SPSS 26.0, where validity tests, reliability tests, classical assumption tests, multiple linear regression analysis, significance tests (t-test), and determination coefficient tests (R Square) were conducted. The results of this study show that the non-physical work environment has a greater influence on employee performance at the Praya Tengah Sub-District Office. Based on the t-test results, it was found that t count &gt; t table (1.993 &gt; 1.697) or the significance value (0.026 &lt; 0.05), indicating that the non-physical work environment has a positive and significant effect on the performance of employees at the Praya Tengah Sub-District Office.

The influence of early life socio-environmental factors on executive performance in a healthy adult sample

Objective Attempts have been made (with research efforts encouraged) to deconstruct the “race” concept into language, cultural, and life experience variables that can help explain performance differences found between ethnic groups (Romero et al., 2009). The extant empirical literature reveals that early environmental factors and life experiences (e.g., socioeconomic status) are related to cognitive test performance in adulthood (Byrd et al., 2006). This study examined the explanatory value of early life childhood resources in the relationship between ethnicity and neuropsychological test performance in adulthood. Participants/ Methods Neurologically and psychologically healthy African American (n = 40), Caucasian (n = 14), and Hispanic (n = 107) college students ranging from 19-38 years of age. On average, participants had completed around 13 years of education, indicating that the majority were in the early stages of their undergraduate studies and mostly consisted of females (72%). Each participant completed a comprehensive neuropsychological battery that included tests of executive function and an extensive background questionnaire. Results A one-way analysis of variance (ANOVA) revealed that the CA group was significantly older (F (2, 160) = 18.38, p = .045) compared to the AA and H groups, but the groups did not differ in terms of number of years of educations or gender. Also, an ANOVA revealed significant group test performance differences on the Stroop-C [F (2, 160) = 1.53, p = .047], but not on the TMT-B and COWAT. Furthermore, a Tukey post hoc revealed that there were no significant differences in test performance on Stroop-C between the groups. Hierarchical multiple regression analyses revealed that group performance differences on executive function tests were medium or non-existent and only partially explained by years of education and early life financial resources. Conclusion The results are discussed in light of the existing literature, study strengths and limitations, as well as directions for future research. This research can aid in pinpointing variables crucial for interpreting differences in neuropsychological assessments among diverse populations, holding potential implications for intervention research and policy settings. It is particularly relevant in the context of the continuously evolving social, political, and economic landscapes of societies.

Enhancing Network Access Control using Multi-Modal Biometric Authentication Framework

This study presents an innovative multi-modal biometric authentication framework that integrates Deep Learning (DL) techniques with zero-trust architecture principles for enhanced network access control. The framework employs a three-tier fusion strategy (feature-level, score-level, and decision-level) incorporating facial, fingerprint, and iris recognition modalities. The system architecture implements a sophisticated multi-layered approach utilizing the ResNet-50 based Convolutional Neural Network (CNN) architecture for facial recognition, CNN-based minutiae extraction for fingerprint processing, and 2D Gabor wavelets with DL-based feature extraction for iris analysis. The experimental validation using established datasets, namely Labeled Faces in the Wild (LFW), CelebA, FVC2004, NIST SD14, CASIA Iris V4, and UBIRIS v2, demonstrates exceptional performance with 99.47% authentication accuracy, 0.02% False Acceptance Rate (FAR), and 0.15% False Rejection Rate (FRR). The framework resulted in a 68% reduction in fraudulent access attempts. It achieved a mean authentication time of 235 ms (SD=28 ms), representing a 45% improvement over traditional systems. The resource efficiency analysis showed significant reductions in system overhead: 32% in CPU utilization, 28% in memory consumption, and 45% in network bandwidth requirements. The scalability testing confirmed a linear performance scaling up to 100,000 concurrent authentication requests. The statistical test of significance through t-test confirmed the framework's significant improvements over existing solutions (p-value&lt;0.001). This study establishes an effective framework to address network access control challenges across various sectors, particularly in high-security environments requiring robust authentication mechanisms.

Nutritional composition, nitrogen-corrected true metabolizable energy, and standardized amino acid digestibility of palm kernel meal from several countries.

Extensive nutritional analyses were conducted for palm kernel meal (PKM) sourced from 5 countries. Two precision-fed rooster trials were conducted to evaluate the nitrogen-corrected true metabolizable energy (TMEn) and standardized amino acid (AA) digestibility of 10 PKM samples (PKM 1 to 10). The TMEn was determined using conventional Single Comb White Leghorn roosters, and standardized AA digestibility was determined using cecectomized roosters. Roosters were fasted for 26 h prior to crop intubation with 25 g of each PKM. Excreta were then collected for 48 h post-intubation and freeze dried for analysis. Statistical analyses were conducted using a one-way ANOVA for a completely randomized design. The least significant difference test was conducted to determine if differences between or among individual treatments were significant at P < 0.05. The PKM samples were found to have an average (DM basis) of 14% CP, 7.5 % fat, and 64% neutral detergent fiber, 0.4% Ca, 0.7% P, and 1.3% phytic acid. Average TMEn for PKM samples was 2082 kcal/kg (DM basis)(range was 1,644 to 2,511). Average Lys, Met, Cys, and Thr digestibility values (%), with ranges in parentheses, for PKM samples were 47 (34 to 60), 73 (66 to 80), 47 (29 to 58), and 65 (54 to 77), respectively. The PKM samples contained highly variable levels of TMEn and digestible AA within and among countries. Nutrient composition also varied substantially among PKM samples.

Assessing the Snow Water Equivalent Dynamics of the Brahmaputra River Basin

ABSTRACT The Brahmaputra River Basin holds a crucial position in South Asia’s hydrological framework, serving as a vital source of water for a substantial population. Snow Water Equivalent (SWE), a pivotal metric reflecting water availability in regions dominated by snow, assumes a significant role in shaping the hydrological cycle of the basin. Thus, this present work aims to conduct a comprehensive study of the SWE dynamics across the Brahmaputra River Basin from 1980 to 2021. The present study obtains the data of elevation, ERA5-Land and SWE and SWE maximum, snowfall, snow cover area, snow depth, and air temperature, in a Geographic Information System (GIS) environment. Seasonal and annual trends are analyzed using the Theil-Sen slope estimator and the Mann-Kendall significance test. Correlations and linear regressions are conducted to assess the relationships among the variables. The findings unveil intricate SWE distribution patterns across the study area, with high elevations exhibiting prolonged snow cover area and substantial SWE values. Trend analysis shows a consistent decrease in SWE and SWE maximum across all seasons, ranging from -0.006 (post-monsoon) to -0.034 m (pre-monsoon) water equivalent/decade. Similarly, a declining trend (at a rate of -0.014 m water equivalent/decade) is also observed in annual SWE. The declining seasonal and annual SWE is possibly linked to escalating air temperature, diminishing snowfall, snow cover area, and snow depth during the study period. The implications of these findings underscore the significance of incorporating SWE variations into water management and the assessment of ecosystem health in the region.

A Target Permutation Test for Statistical Significance of Feature Importance in Differentiable Models

A Target Permutation Test for Statistical Significance of Feature Importance in Differentiable Models

Too hard to die: an empirical overview of the post-pandemic resilience and financial performance of the Italian fashion industry

PurposeResearchers have paid limited attention to how the fashion sector has evolved in the years following the pandemic. This study aims to address this gap by providing an overview of the Italian fashion sector and its financial performance related to the concept of resilience.Design/methodology/approachThe model is based on a segmentation analysis of 5,129 firms in the Italian fashion sector, utilizing financial variables such as return on equity and return on sales. Moreover, it employs significance tests with the aid of Levene’s test and ANOVA.FindingsIt was discovered that the debt ratio, operating cash flow and aggregate growth ratio (AGR) over a five-year period exhibit significant differences across clusters. Additionally, it was determined that the debt ratio and operating cash flow are key financial indicators of firm resilience. These data have confirmed the resilience of the Italian fashion sector.Originality/valueThis study is among the first to focus on the financial performance of the Italian fashion sector, its resilience and post-pandemic recovery, as well as employing a reverse engineering system to identify the most suitable financial indicators for defining a sector’s resilience.

Prevalence and Associated Factors of Infertility among Married Couples of Kunnathukal Panchayath in Trivandrum District

Abstract Background: Infertility is a serious health issue worldwide, and it has been a neglected area among studies related to maternal and child health. There is a need for evidence-based knowledge regarding the prevalence of infertility and its determinants. The aim of this study was to estimate the prevalence of infertility and its associated factors among married couples residing at Kunnathukal Panchayath in Trivandrum district. Materials and Methods: A community-based cross-sectional study was conducted among 1270 married couples residing at Kunnathukal Panchayath, selected using a cluster sampling technique. Data was collected using a pretested predesigned semi-structured questionnaire, entered into MS Excel, and analyzed using SPSS version 20. The Chi-square test, Fischer’s exact test, and Mann–Whitney U test were used as tests of significance. Binary logistic regression analysis was performed to find independent predictors of infertility. P value &lt; 0.05 was considered as statistical significance. Results: Prevalence of infertility was found to be 12%, and among them, majority (65.8%) had primary infertility. The factors found to be significantly associated with infertility are occupation of females, age at time of menarche, dysmenorrhea, type of dysmenorrhea, dyspareunia, fibroid uterus, hypothyroidism, and BMI. In multivariate analysis, dysmenorrhea, type of dysmenorrhea, and uterine fibroid emerged as predictors of infertility. Conclusion: Prevalence of infertility was found to be 12% in this study, which is almost similar to our state’s average (10.5%) according to DLHS-3. Dysmenorrhea, type of dysmenorrhea, and uterine fibroid emerged as predictors of infertility. The preventive and intervention activities addressing infertility should give more priority to these predictors.

Effectiveness of Chinese Betel Leaf Extract (Peperomia pellucida L) on the Growth of Propionibacterium acnes Bacteria

Propionibacterium acnes bacteria cause acne by producing lipase enzymes that break down free fatty acids in skin lipids. This study aimed to determine whether or not there is antibacterial activity from the extract of the Chinese betel leaf plant (Peperomia pellucida L) against Propionibacterium acnes and to determine the concentration of the inhibition zone. The study was experimental with 25%, 50%, 75% and 100% extract concentrations. The method used to test antibacterial activity in this study was the disc diffusion method, namely using discs planted on Nutrient Agar (NA) media. The maceration of Chinese betel leaves (Peperomia pellucida L) is the Chinese betel leaf extraction method. The results showed that at a concentration of 25% Chinese betel leaf extract, the results were 22.5 mm; at a concentration of 50% extract, the inhibition zone was 24 mm; at a concentration of 75% Chinese betel leaf extract, the inhibition zone was 30 mm, at a concentration of 100% Chinese betel leaf extract, the inhibition zone was 20 mm. The inhibition zone of the positive control using the antibiotic chloramphenicol was obtained at 10 mm; in the negative control using sterile distilled water, there was no inhibition zone. The results of the significance test of the Chinese betel leaf extract (Peperomia pellucida) from the Greenwood classification results in the Post hoc test show that the difference between concentrations can be stated as significant at each concentration.

P-168. Antimicrobial Resistance Patterns at a Private Referral Hospital in Blantyre, Malawi

Abstract Background Antimicrobial resistance (AMR) is expected to be a global health security risk in Africa in coming decades. While AMR data is available from Malawi public hospitals, data from Malawi’s burgeoning private hospital sector is limited. Initial Hospital Antibiogram Data, Fall 2021 - Spring 2022 Methods An antibiogram was obtained for clinical blood and urine cultures from October 2021 through April 2022 at a private referral hospital in southern Malawi (n=684). Logistic regression was used to assess for patient-related determinants of AMR, as well as effects on patient outcomes. Tests of Association for AMR Determinants Results High AMR rates were seen in E. coli (37.4% ceftriaxone, 35.8% ciprofloxacin, 32.5% trimethoprim), as well as unexpected carbapenem resistance (7.4% of tested isolates). Statistically significant tests of association linked both fluoroquinolone (X2 = 4.91, p=0.027) and third-generation cephalosporin resistance (X2 = 3.67, p=0.05) to patients who had increased numbers of antecedent hospital encounters. Fluoroquinolone resistance was associated with increased risk for acute-care follow up within 30 days of antibiotic prescription (X2 = 4.73, p=0.03). Third-generation cephalosporin resistance (suggestive of ESBL production) showed association with older age (X2 = 20.009, p=&amp;lt; 0.001) and female gender (X2 = 5.58, p=0.018) A multiple logistic regression model examining variables associated with ceftriaxone-resistant Enterobacterales showed increased risk of patient death (OR 5.99, CI 1.37–26.17, p=0.017), as well as higher Charlson comorbidity index (OR 1.14, CI 1.02-1.28, p=0.024). Over 40% of patients with culture-positive results were treated empirically, without follow up for review of sensitivities. Choice of empiric antibiotic differed from national guidelines, with only 32% UTI patients and 8.4% pharyngitis patients receiving recommended first-line regimens. Local point-prevalence data sometimes appeared to be discordant with national data. As one example, all three recommended cystitis regimens on national formulary guidelines displayed &amp;gt;=30% resistance rates on local antibiogram data, including ciprofloxacin, nitrofurantoin, and cefuroxime. Logistic Regression for Determinants of ESBL Enterobacterales Multiple logistic regression model Conclusion Further research needs to assess best practices for stewardship in regional private hospitals, as well as determinants for AMR that encompass markers of healthcare utilization. Demographic Descriptive Statistics Disclosures All Authors: No reported disclosures

Productivity and Profitability of Maize-Mungbean and Maize-Chili Pepper Relay Intercropping Systems for Income Diversification and Soil Fertility in Southern Benin

Low vegetable consumption in sub-Saharan Africa partly arises from limited availability across cereal-based zones. A field experiment in southern Benin (April to September 2023) evaluated four maize–chili and five maize–mungbean relay intercropping. Growth and yield data and farmers’ perceptions were analyzed using analysis of variance with the least significant difference test, land equivalent ratio (LER) and monetary indexes. Maize grain yield was statistically similar across patterns, whereas chili and mungbean yields differed significantly. All sowing patterns achieved LER &gt; 1. Pattern (1:1) maize–chili had a modest LER (1.15), while treatment (1:3) had a high LER (1.60) for mungbean–maize. Both patterns showed high actual yield gain and intercropping advantage. Pattern (2:2) for maize–chili and pattern (1:3) for maize–mungbean yielded the greatest gross return (7796.6 USD/ha and 1301.2 USD/ha, respectively). Sole mungbean and all intercropping sowing patterns significantly increased mineralizable carbon. Pattern (1:3) maize-mungbean slightly increased total nitrogen and potassium. Farmers ranked the highest pattern (2:2) for maize–chili and (1:3) for maize–mungbean due to sup erior weed, water, and soil management and increased yields. These findings suggest that diversified maize systems incorporating chili pepper and mungbean offer economic benefits and better soil health in southern Benin.

Critical Thinking Ability in a STEM-Based Problem-Based Learning Model: A Focus on Temperature and Heat

This study explores enhancing students' critical thinking skills by applying a STEM-centered problem-based learning (PBL-STEM) model. It evaluates students’ responses to this pedagogical approach. A quasi-experimental design with a static group pretest-posttest framework was employed, involving secondary school students from Merauke City. The instrument used for data collection was a Critical Thinking Ability Test, which included 10 essay questions covering six subtopics related to temperature and heat and incorporated five indicators of critical thinking. The test's reliability, as measured by Cronbach's alpha, was 0.739. Additionally, student responses to the PBL-STEM method were gathered through questionnaires. Data were analyzed both descriptively and inferentially. The results indicated that implementing PBL-STEM was effective, significantly improving students' critical thinking skills, with an N-gain of 0.824, classified as high. Furthermore, the significance test revealed that the enhancement in critical thinking through PBL-STEM was greater than that achieved through traditional PBL methods.

Iron Status of Pregnant Women at Nsukka, Enugu State, South East Nigeria

Background: Anaemia is a serious health issue, particularly in remote areas such as Nsukka, Enugu State, South East geopolitical zone of Nigeria as a result of poor awareness. A comprehensive study on the iron status of pregnant women attending antenatal clinic in Nsukka, is rare. Objectives: The study aimed to evaluate the iron status of pregnant women living at Nsukka in Enugu State, South, East Nigeria via monitoring prevalence and severity of anaemia in pregnant women in first, second and third trimester using haemoglobin and ferritin concentration as bio- indicators of anaemia among the subjects, effect of age, gestational age and intake of routine drugs on haemoglobin and ferritin concentration of the subjects. Methods: A cross-sectional study was conducted involving 127 pregnant women, within the ages of 20-49years, grouped based on the stages of pregnancy namely first trimester 30 pregnant women (23.6%), second trimester 40 pregnant women 31.5%, and third trimester 57 pregnant women (44.9%), attending antenatal clinic in Nsukka Divisional Health Centre were recruited for the study using simple random technique. All data and statistical analyses were conducted using Statistical Package for Social Sciences software, version 27. Descriptive statistics such as means and percentages were calculated and reported by groups. Test of statistical significance was carried out using One-Way Analysis of Variance (ANOVA). Pearson test was performed to assess the correlation between haemoglobin, ferritin concentrations with other stated variables. Results: The age distributions of the 127 pregnant women were 20 – 24 years (26.8%), 25 – 29 years (37%), 30 – 34 years (20.4%), 35 – 39 years (12.6%), 40 -44 years (2.4%) and 45 – 49 years (0.8%). Study revealed that 39.4% of the pregnant women were anaemic because their haemoglobin concentration was less than 11 g/dl. Exactly 27.6% of women were anaemic based on ferritin concentration below 15 µg/l. None of the subjects were anaemic in first trimester, 26% of the subjects were anaemic in second trimester while 11.3% of the subjects were anaemic in third trimester. Based on ferritin level alone, 66.6% of the subjects were anaemic in first trimester, 46% in second trimester while 53.5% of the subjects in third trimester were anaemic. Conversely, 16.7% of the subjects in first trimester, 22% in second trimester and 23.9% in third trimester were anaemic based on low haemoglobin and ferritin level respectively. Haemoglobin concentration significantly (p &lt; 0.05) showed negative correlation with age effect and positive correlation with ferritin concentration although not significant (p &gt; 0.05). Gestational age correlated with haemoglobin and ferritin concentration showed negative correlation which was not significant (p &gt; 0.05). Intake of routine drug correlated with haemoglobin and ferritin respectively showed negative and positive correlation respectively, although not significantly different (p &gt; 0.05). Conclusion and Recommendations: Maternal iron status is heavily affected by pregnancy even in a high-resource, generally iron-supplemented population. Our study revealed that anaemia during pregnancy was highly prevalence in Nsukka, Enugu State because more than halve of the subjects screened were anaemic in both first, second and third trimester. Early screening of pregnant women for iron status using serum or plasma ferritin standard measure of iron deficiency according to the world Health Organization is highly encouraged. There is a need to educate and implement strategies that will improve iron status of pregnant women for the safety of the mother and the unborn child.

Effectiveness of RESET care program: A real-world-evidence on managing non-alcoholic fatty liver disease through digital health interventions.

Non-alcoholic fatty liver disease (NAFLD) management requires sustainable lifestyle modifications. This study aimed to evaluate the effectiveness of the RESET care plan, a comprehensive program that is an integrated personalized diet, exercise, and cognitive behavior therapy, delivered via MyTatva's digital health application enabled through a body composition analyzer (BCA) and smartwatch. To evaluates the effectiveness of the comprehensive program delivered via MyTatva's digital health app enabled through internet of thing devices. This retrospective observational study analyzed deidentified data from 22 participants enrolled in the MyTatva RESET care program. Participants were divided into three groups: Group A, diet plan; Group B, diet + exercise plan; and Group C, diet + exercise + cognitive behavioral therapy plan. Participants were provided with a BCA and smartwatch for continuous monitoring of anthropometric parameters. Statistical analysis, including one-way ANOVA and post-hoc Tukey's Honest Significant Difference test, was conducted to compare mean changes in anthropometric parameters across the groups. All intervention groups showed significant improvement across all anthropometric parameters. Group C showed the most significant improvements, with mean weight reduction of 7% or more (6.99 ± 2.98 kg, 7.00% ± 3.39%; P = 0.002) from baseline, a benchmark associated with improved NAFLD conditions. Post-hoc analysis revealed that Group C had significantly greater improvements than Groups A and B. Weight reduction was observed in 85.7% of Group A participants, 77.8% of Group B participants, and 100% of Group C participants. The comprehensive RESET care plan achieved a 7% weight reduction in 12 weeks, demonstrating its effectiveness in managing NAFLD. These results support adopting digitally supported, patient-centric approaches for NAFLD treatment.

Assessment of Autonomic Nervous System Function in Patients with Chronic Fatigue Syndrome and Post-COVID-19 Syndrome Presenting with Recurrent Syncope

Background/Objectives: Chronic fatigue syndrome and post-COVID-19 syndrome are associated with dysfunction of the autonomic nervous system, which may manifest as syncope and orthostatic intolerance. This study aimed to compare autonomic nervous system function in patients with chronic fatigue syndrome of unknown etiology and those with chronic fatigue syndrome secondary to post-COVID-19 syndrome using multiple diagnostic modalities, and to assess the prevalence and characteristics of syncope in these populations. Methods: This cross-sectional study included 440 patients examined at the Neurocardiological Laboratory of the Institute for Cardiovascular Diseases “Dedinje”. Patients were divided into three groups: chronic fatigue syndrome of unknown etiology (Group 1, n = 210), chronic fatigue syndrome secondary to post-COVID-19 syndrome (Group 2, n = 137), and healthy controls (Group 3, n = 91). Diagnostic modalities included cardiovascular reflex tests, the head-up tilt test, beat-to-beat analysis, 24 h Holter electrocardiogram monitoring, and 24 h ambulatory blood pressure monitoring. Statistical analyses were performed using analysis of variance, Tukey’s honestly significant difference test, and the Mann–Whitney U test. Results: Both chronic fatigue syndrome groups demonstrated significant autonomic nervous system dysfunction compared to healthy controls (p &lt; 0.05), including reduced baroreceptor sensitivity and impaired heart rate variability parameters. Syncope prevalence was high in both chronic fatigue syndrome groups, with extreme blood pressure variability observed in 45–47% of patients during the head-up tilt test. Patients with post-COVID-19 chronic fatigue syndrome exhibited greater blood pressure increases during the head-up tilt test than those with chronic fatigue syndrome of unknown etiology (p &lt; 0.05). Conclusions: Patients with chronic fatigue syndrome, irrespective of etiology, exhibit significant autonomic nervous system dysfunction and a high prevalence of syncope. Post-COVID-19 chronic fatigue syndrome demonstrates distinct hemodynamic patterns, suggesting unique pathophysiological mechanisms that warrant further investigation.